Description:
Here we have a comprehensive, problem-oriented, engineering perspective on the uses of neural nets, fuzzy systems, and hybrids that emphasizes practical solutions to everyday artificial intelligence (AI) problems over abstract theoretical noodling. Intended for upper-division students and postgraduates who need a solid grounding in knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering is still useful as a reference for professionals and even as a text for advanced students in the lower levels. Taking problems as diverse as soil classification and speech recognition, Kasabov shows the relative merits and failings of the different architectures and hybrids through examples and problems to solve. Organized as a textbook, the first two chapters cover the field as a whole and present traditional AI approaches to knowledge engineering. Subsequent chapters examine the particulars of fuzzy systems, neural networks, hybrids, and new models. Foundations assumes a good understanding of undergraduate-level mathematics; those who wish to fully explore the problems on their own can obtain the requisite software for free through anonymous FTP. --Rob Lightner
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